The-Strategy-Unit / The-Strategy-Unit/data_science
Github actions
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session: C&C ☕
- Dominant language
- Jupyter Notebook
- Stars
- 11
- Forks
- 5
- Avg merge
- 5h 34m
- Merged PRs (30d)
- 1
Description
- Sound fun, but also intimidating, tell me that they aren’t
- How do I use them?
- What are some good, bad, indifferent, ones?
- Continuous integration? What is it? What is it good for what is it not good for?
- Examples we have in the Data Science team’s repos (Matt)
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the GitHub Actions examples in the Data Science team's repositories and the questions listed in the issue. Done means a clear guide explains what GitHub Actions and continuous integration are, how to use them, their trade-offs, and relevant repository examples.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- github-actions
- Domain
- ci-cd, documentation
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100